References |
: |
[1]Mohamed H, Abdulsalam L, Mohammed H. Adaptive genetic algorithm for improving prediction accuracy of a multi-criteria recommender system. In international symposium on embedded multicore/many-core systems-on-chip 2018 (pp. 79-86). IEEE.
|
[Crossref] |
[Google Scholar] |
[2]Hassan M, Hamada M. Performance analysis of neural networks-based multi-criteria recommender systems. In international conferences on information technology, information systems and electrical engineering 2017 (pp. 490-4). IEEE.
|
[Crossref] |
[Google Scholar] |
[3]Ze W, Dengwen Z. Optimization collaborative filtering recommendation algorithm based on ratings consistent. In international conference on software engineering and service science 2016 (pp. 1055-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[4]Rodrigues CM, Rathi S, Patil G. An efficient system using item & user-based CF techniques to improve recommendation. In international conference on next generation computing technologies 2016 (pp. 569-74). IEEE.
|
[Crossref] |
[Google Scholar] |
[5]Ying Y, Cao Y. Collaborative filtering recommendation combining FCM and slope one algorithm. In international conference on informative and cybernetics for computational social systems 2015 (pp. 110-5). IEEE.
|
[Crossref] |
[Google Scholar] |
[6]Gupta J, Gadge J. Performance analysis of recommendation system based on collaborative filtering and demographics. In international conference on communication, information & computing technology 2015 (pp. 1-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[7]Wei S, Ye N, Zhang S, Huang X, Zhu J. Collaborative filtering recommendation algorithm based on item clustering and global similarity. In international conference on business intelligence and financial engineering 2012 (pp. 69-72). IEEE.
|
[Crossref] |
[Google Scholar] |
[8]Shambour Q, Hourani M, Fraihat S. An item-based multi-criteria collaborative filtering algorithm for personalized recommender systems. International Journal of Advanced Computer Science and Applications. 2016; 7(8):274-9.
|
[Google Scholar] |
[9]Hassan M, Hamada M. A neural networks approach for improving the accuracy of multi-criteria recommender systems. Applied Sciences. 2017; 7(9):1-18.
|
[Crossref] |
[Google Scholar] |
[10]Adomavicius G, Kwon Y. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems. 2007; 22(3):48-55.
|
[Crossref] |
[Google Scholar] |
[11]Konstan JA, Riedl J. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction. 2012; 22(1-2):101-23.
|
[Crossref] |
[Google Scholar] |
[12]Zhu X, Ye H, Gong S. A personalized recommendation system combining case-based reasoning and user-based collaborative filtering. In Chinese control and decision conference 2009 (pp. 4026-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[13]Zarzour H, Al-Sharif Z, Al-Ayyoub M, Jararweh Y. A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In international conference on information and communication systems 2018 (pp. 102-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[14]Zhang H, Ganchev I, Nikolov NS, ODroma M. A trust-enriched approach for item-based collaborative filtering recommendations. In international conference on intelligent computer communication and processing (ICCP) 2016 (pp. 65-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[15]https://grouplens.org/datasets/movielens/. Accessed 12 March 2019.
|
|